Instructions to use suzuki-2001/BERT-K7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K7")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K7") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bf768fd6746720c85239d2a47d0000313cc85b185eea02c34a27bfc1c9a4989
- Size of remote file:
- 4.86 kB
- SHA256:
- 8c53c7be035c7de9fa73bcbcaf25444472ac67737dc3ea73f385f0c49bfe4d5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.